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109 results for “anomalous”
Magnetism and anomalous transport in the Weyl semimetal PrAlGe: Possible route to axial gauge fields
<p>The file ManuscriptDataFiles.7z contains the raw experimental data from which the figures are made in the manuscript entitled "Magnetism and anomalous transport in the Weyl semimetal PrAlGe: Possible route to axial gauge fields" that appeared in npj Quantum Materials <strong>5</strong>, 5 (2020).</p> <p>Paper Abstract: In magnetic Weyl semimetals, where magnetism breaks time-reversal symmetry, large magnetically sensitive anomalous transport responses are anticipated that could be useful for topological spintronics. The identification of new magnetic Weyl semimetals is therefore in high demand, particularly since in these systems Weyl node configurations may be easily modified using magnetic fields. Here we explore experimentally the magnetic semimetal PrAlGe, and unveil a direct correspondence between easy-axis Pr ferromagnetism and anomalous Hall and Nernst effects. With sizes of both the anomalous Hall conductivity and Nernst effect in good quantitative agreement with first principles calculations, we identify PrAlGe as a system where magnetic fields can connect directly to Weyl nodes via the Pr magnetization. Furthermore, we find the predominantly easy-axis ferromagnetic ground state co-exists with a low density of nanoscale textured magnetic domain walls. We describe how such nanoscale magnetic textures could serve as a local platform for tunable axial gauge fields of Weyl fermions.</p>
ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period on 10 April 2009. </p> <p>The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (°), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (°) and computed specific differential phase (° km<sup>-1</sup>) data (Hubbert and Bringi 1995). </p> <p> </p> <p>ARMOR file naming conventions are as follows: </p> <p> </p> <p>RAW_NA_000_125_20090410171216.gz</p> <p>RAW: file format</p> <p>125: can scan type, where 125 indicates a full or sector volume plan position indicator </p> <p>20090410171216: date and time in the order of year, month, day, hour, minute, and second</p> <p> </p> <p>ARMOR_20090410171216_qc1.uf.gz</p> <p>ARMOR: radar name</p> <p>20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS)</p> <p>qc1: denotes ARMOR processed data</p> <p>uf: denotes the file format </p> <p> </p> <p>NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics.</p> <p>Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi<sup>2</sup> value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). </p> <p> </p> <p>Hourly file naming conventions are as follows:</p> <p> </p> <p>LYLOUT_090410_160000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>090410: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>160000: time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>Acknowledgments: </p> <p>Data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p>
ARMOR and NALMA data corresponding to 2008 storms analyzed in "Examining conditions supporting the development of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Total lightning and dual-polarization Doppler velocity data are available from the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) and the C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR), respectively, over selected periods on 6 February 2008 and 11 April 2008. NALMA data are provided in American Standard Code for Information Interchange (ASCII) format and ARMOR data are provided in Raw and quality-controlled Universal Format (UF), where quality control methods are described below. </p> <p> </p> <p>The NALMA data are provided in hourly files which include undecimated point location (source-level) data corresponding to the detection of very high frequency (VHF) radiation emitted during the breakdown of lightning (Rison et al., 1999; Thomas et al., 2001). Source locations were reported from active sensors configured in an 11-sensor array distributed throughout North Alabama and South Central Tennessee, the center of which is located at 34.72641, -86.64533 (Koshak et al. 2004). Data files include information on the time that each source was detected (UTC seconds of the day), the latitude, longitude, and altitude of each source’s location (decimal degrees and m, respectively), the reduced chi<sup>2</sup> value associated with data processing (unitless), a station mask indicating which sensors contributed to the resolved location of each source (unitless). These data are provided in a line-by-line format of (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). The 2008 data files additionally include a header section that provides further information about each sensor in the network and its relative contribution to the dataset. </p> <p> </p> <p>The hourly fine naming conventions are as follows for the February 2008 data:</p> <p>LMA_NA_6.2_125_2008-02-06_10-00-00.dat.gz</p> <p>LMA_NA: LMA file designator corresponding to the NALMA</p> <p>2008-02-06: year (YYYY)-month (MM)-day (DD)</p> <p>10-00-00: UTC time, (HH)-minute (MM)-second (SS)</p> <p> </p> <p>And for the April 2008 data:</p> <p>LYLOUT_080411_180000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>080411: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>180000: UTC time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>ARMOR data are provided as sets of 14 (14) sampling volumes corresponding to the 6 February 2008 (11 April 2008) periods between 1002 UTC and 1119 UTC (1844 UTC and 1952 UTC). Each RAW and processed UF file contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (º), and total power (dBZ) data. Horizontal reflectivity and differential reflectivity data were corrected for attenuation and differential attenuation, differential propagation phase (º) was estimated, and specific differential phase (º km<sup>-1</sup>) was calculated during post-processing (Hubbert and Bringi 1995, Bringi et al. 2001).</p> <p> </p> <p>Acknowledgments: </p> <p>NALMA data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p> <p>Thomas, R. J., Krehbiel, P. R., Hamlin, T., Harlin, J., & Shown, D. (2001). Observations of VHF source powers radiated by lightning. <em>Geophysical Research Letters</em>, <em>28</em>(1), 143–146. https://doi.org/10.1029/2000GL011464</p> <p> </p>
Peeking under the canopy: anomalously short fire-return intervals alter subalpine forest understory plant communities
Changing climate and fire regimes are profoundly affecting temperate coniferous forests, driving greatly reduced tree cover postfire. However, whether similar changes are present in the understory of these forests remains less well-understood. We sampled understory plant communities in 20 plot pairs across Greater Yellowstone (Wyoming, USA) in July and August 2021, with each including one plot burned at short (<30 year) fire-return interval and one plot burned in the same most recent fire but not burned previously for >125 years. We also included 11 plot pairs meeting our definition of short- and long-interval fire that were sampled 12 years after the 1988 Yellowstone fires in summer 2000. We also used previously collected published and unpublished data to compare understory communities following recent (2016) short-interval fires to those following the previous long-interval fire in the same general area. In each plot, percent cover of understory plant species was estimated in 0.25-m2 quadrats, and species richness determined via a whole-plot sweep. Understory plant community cover, richness, and diversity did not differ by interval class, but species able to persist in drier conditions and in lower vegetation zones became more abundant following shot interval fire. Further, previously distinct understory communities following long-interval fire in two regions of Greater Yellowstone became slightly more similar following recent short-interval fire. Dissimilarity between plot pairs increased with greater historical snowfall and decreased with time since fire and postfire winter snowfall. These changes to understory plant communities may continue with ongoing shifts in climate and fire across temperate and boreal forests.
Dataset for A Statistical Survey of E-region Anomalous Electron Heating Using Poker Flat Incoherent Scatter Radar Observations
<p>This archive contains the complete list of anomalous electron heating (AEH) events in PFISR data between 2010 and 2023 identified by Zhang and Varney (2024), along with the code necessary to reproduce the results. The main list of AEH events is in the file AEH_event_list.csv, and the rest of this archive is supporting information for reproducibility.</p> <p>The files contained are:</p> <p>algo1.ipynb: Python notebook implementing algorithm 1.</p> <p>algo2.py: Python script implementing algorithm 2.</p> <p>algo3.ipynb: Python notebook implementing algorithm 3.</p> <p>algo4.ipynb: Python notebook implementing algorithm 4.</p> <p>cal_velo.py: Python function to calculate ion velocity.</p> <p>io_utils.py: Python functions for manipulating AMISR hdf5 files.</p> <p>Fig1.ipynb: Python notebook to recreate figure 1.</p> <p>Fig2,5.ipynb: Python notebook to recreate figures 2 and 5.</p> <p>Fig3,11.ipynb: Python notebook to recreate figures 3 and 11.</p> <p>Fig4.ipynb: Python notebook to recreate figure 4.</p> <p>Fig6.ipynb: Python notebook to recreate figure 6.</p> <p>Fig7,8,9,10.ipynb: Python notebook to recreate figures 7, 8, 9, and 10.</p> <p>PFISR_Data_Quality_Checker.ipynb: Python notebook with data preprocessing and quality checking.</p> <p>Table1.ipynb: Python notebook to extract the beamcode information needed for table 1.</p> <p>AEH_events_list.csv: Complete list of AEH events identified by algorithms 1, 3, and 4. The first column indicates the UT time of the start of the event, and 1 or 0 in the three columns denote whether the event was or was not detected by the algorithm, respectively.</p> <p>AEH_in_2010&2011.csv: Spreadsheet to facilitate direct comparisons with previous work on AEH in 2010 and 2011.</p> <p>f107.json: Smoothed F10.7 data used in this study.</p> <p>AEH_Detection_Outputs.zip: Archive of all of the raw output of the python scripts running the detection algorithms.</p> <p>AE&PAE.zip: Archive of all AE data used in this study.</p>
GRN_MARVEL_ANOMALOUS_TRAJECTORIES
<p>The raw audio-video data was collected from Mgarr, a rural town on the western coast from IP cameras. Data has been manually annotated for anomalous trajectories. Annotation data in the form of a CSV file.</p>
Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020
<p>The datasets are for the article 'Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020', including the WRF Initial conditions (horizontal wind at 850 hPa, geopotential height at 850 and 200 hPa) of sensitivity experiments (CTRL, W_WNPSH, W_SAH, W_TUTT, and S_SASM) in July 2020, and the formation records of the simulated TC formation in all experiments.</p>
Near-range atmospheric dispersion dataset of an anomalous selenium-75 emission
<p><strong>Description</strong></p> <p>This dataset contains the different measurement data that were obtained during and in the wake of the anomalous selenium-75 (Se-75) emission from the Belgian Reactor 2 (BR2) in 2019. These data were analysed by Frankemölle <em>et al</em> (2022), who showed that they paint a consistent picture of the Se-75 emission on both the time scale of the initial puff as well as on the time scale of the residual release. In the first tab of the Excel file, the different data included in the rest of the excel file are briefly discussed.</p> <p><strong>Included data</strong></p> <ul> <li>On-site meteorological data</li> <li>In-stack measurements of Se-75 source term</li> <li>On-site measurements of ambient dose equivalent rates</li> <li>On-site deposition measurements</li> <li>On-site concentration measurements</li> </ul> <p><strong>Original publication</strong></p> <p>Frankemölle, J.P.K.W., Camps, J., De Meutter, P., Antoine, P., Delcloo, A.W., Vermeersch, F. and Meyers, J. (2022) 'Near-range atmospheric dispersion of an anomalous selenium-75 emission', <em>Journal of Environmental Radioactivity, </em>255<em>, </em>pp. 107012. DOI: <a href="https://doi.org/10.1016/j.jenvrad.2022.107012">https://doi.org/10.1016/j.jenvrad.2022.107012</a></p>
Propagation, dissipation and breakdown in quantum anomalous Hall edge states probed by microwave edge plasmons
<p>Here we upload the raw data from the manuscript entitled “Propagation, dissipation and breakdown in quantum anomalous Hall edge states probed by microwave edge plasmons ”, by T. Röper, H. Thomas, D. Rosenbach, A. Uday, G. Lippertz, A. Denis, P. Morfin, A.A. Taskin, Y. Ando and E. Bocquillon. We provide Jupyter notebooks to load, process, and plot all results. The datasets contain measurements on 4 devices. Each device has its own Jupyter notebook. The necessary Python packages are listed in the file called "requirements.txt". <br><br><br></p>
Dataset related to the publication "Anomalous radiative transfer in heterogeneous media"
<p>This repository contains Monte Carlo results for anomalous light transport in a spherical geometry with homogeneous (<code>Hom</code>), heterogeneous layered (<code>Lay</code>), and homogeneous layered (<code>VHom</code>) configurations.</p> <p>Part of these data is described in the publication:</p> <p>F. Tommasi et al. "Anomalous radiative transfer in heterogeneous media" <em>Advanced Theory and Simulations</em> (2024) <a href="https://doi.org/10.1002/adts.202400182">https://doi.org/10.1002/adts.202400182</a></p> <p><br>Data is organized in two types of csv files:</p> <ul> <li><code>filename.csv</code> containing information on the average path length and total path length distribution</li> <li><code>filename_Fluence.csv</code> containing information on the fluence rate and radiance at each spherical layer boundary</li> </ul> <p>Filenames contain information on the simulation parameters used:</p> <table> <tbody> <tr> <td><code>ISO</code></td> <td>isotropic scattering</td> </tr> <tr> <td><code>K0_X</code></td> <td>value of the k coefficient of the Generalized Pareto Distribution used in the Monte Carlo simulation (<code>X</code> = 0.3 or 0.7)</td> </tr> <tr> <td><code>1e7</code></td> <td>number of total trajectories considered</td> </tr> <tr> <td><code>DS</code>, <code>noDS</code></td> <td>simulation performed used the proposed set of rules for anomalous transport in bounded media (<code>DS</code>) or with the standard set of rules (<code>noDS</code>).</td> </tr> <tr> <td><code>Hom</code></td> <td>homogeneous sphere configuration</td> </tr> <tr> <td><code>NLay</code></td> <td>number of layers used in the layered configurations</td> </tr> <tr> <td><code>mism, no_mism</code></td> <td>configuration with (<code>mism</code>) or without (<code>no_mism</code>) refractive index mismatch with the environment or between layers</td> </tr> <tr> <td><code>Vhom_layN</code></td> <td>layered homogeneous sphere configuration with N layers</td> </tr> <tr> <td><code>mus_const</code>, <code>mus_step</code></td> <td>simulations performed using a constant scattering coefficient (<code>mus_const</code>) or different values across different layers (<code>mus_step</code>)</td> </tr> </tbody> </table> <p> </p> <p>The header of each output file contains general information on the simulation settings as detailed below:</p> <ul> <li>The number of layers</li> <li>The radius of each layer</li> <li>The refractive index of each layer and of the external region</li> <li>The critical angle for entering and exiting the sphere</li> <li>Reduced scattering coefficient of each layer</li> <li>Scattering function</li> <li>Type of illumination source</li> <li>Absorption coefficient of the sphere</li> </ul> <p>Simulations for different scattering strengths are classified based on the optical thickness <code>Taud</code> of the sphere, corresponding to the product between the sphere diameter (10 mm) and the reduced scattering coefficient. Since we have results only for isotropic scattering, <code>Taud</code> is also the product of diameter and scattering coefficient.<br>For a layered sphere, <code>Taud</code> is given by the sum of the products between the scattering coefficient and radial thickness of each layer.<br>The symbol <code>Taua</code> is also used to denote the product between the sphere and the absorption coefficient. Since we have considered only non-absorbing media, this value is always zero.<br>Each simulation is carried out for different values of the reduced scattering coefficient in the layers, keeping their reciprocal ratios fixed, resulting in the following values of Taud: 1E-3, 2E-3, 5E-3, 1E-2, 2E-2, 5E-2, 1E-1, 2E-1, 5E-1, 1, 2, 5, 10, 20, 50, 100, which, in the case of the homogeneous sphere, correspond to reduced scattering coefficients comprised between 1E-4 and 10.<br>The standard error (<code>SE</code>) is also provided, based on the results of 100 independent simulations with 1E5 trajectories each, for a total of 1E7 total trajectories considered.</p> <h3>Structure of the pathlength files (<code>filename.csv</code>)</h3> <p>List of content for the calculated quantities shown:</p> <ul> <li><code>Ksc_Max</code>: maximum number of scattering events in the medium</li> <li><code>de_max(mm)</code>: maximum pathlength followed by photons</li> <li>Number of Photons lost (always zero, all trajectories are collected)</li> <li>Total CW Exiting Radiation and Standard Error (SE)</li> <li>Mean Pathlength for Total Exiting Radiation and SE</li> <li>Solutions of the RTE for the non-absorbing sphere with Lambertian illumination given in terms of the mean pathlength in the sphere and partial pathlength in each layer</li> <li>Partial Mean Pathlengths for in each layer for the non-absorbing case</li> <li>Standard Error on Partial Mean Pathlengths in each layer</li> <li>Discrepancy and SE for the Pathlengths for the non-absorbing case</li> <li>Relative SE is shown for the Partial Mean Pathlengths in the non-absorbing case </li> <li>Total Spread Function (mm-1) versus the pathlength of emerging photons l (mm) for each Taud value</li> </ul> <h3>Structure of the fluence files (<code>filename_Fluence.csv</code>)</h3> <p>List of content for the calculated quantities shown:</p> <ul> <li>Mean pathlength and partial pathlength</li> <li>CW fluence at each layer boundary</li> <li><code>SE</code>: standard error for the CW fluence</li> <li>Discrepancy and SE of the CW fluence with respect to the expected value from the invariance properties. This is reported for each layer interface</li> <li>Relative Error of the CW fluence</li> <li><code>CW FLUX_P</code> at each layer boundary</li> <li>Distribution of CW radiance at each layer boundary for all <code>Taud</code> values. The distribution expected from the invariance property is also listed in the columns adjacent to the Monte Carlo results. Radiance data is reported as a function of the angle at each layer boundary</li> </ul> <p>Additional empty fields in the files refer to output quantities that can be optionally calculated during the simulations. These options were not selected for this study.</p> <p>For convenience, we list below which files were used to prepare each Figure (some files are used multiple times for different Figures):</p> <h3>Figure 2</h3> <p><code>Hom/ISO_Hom_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_3_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_3_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_7_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_7_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_3_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_3_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_7_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_7_1e7_noDS.csv</code></p> <h3>Figure 3</h3> <p><code>Lay/ISO_10lay_mism_k0_3_1e7_DS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_3_1e7_noDS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_7_1e7_DS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_7_1e7_noDS_Fluence.csv</code></p> <h3>Figure 4</h3> <p><code>Hom/ISO_Hom_no_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_7_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_noDS.csv</code></p> <h3>Figure 5</h3> <p><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay2_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay4_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay8_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay16_mism_k0_7_1e7_DS.csv</code></p>
Multiwavelength anomalous dispersion (MAD) diffraction images of Burkholderia pseudomallei BipD.
<p>Selenomethionine MAD diffraction images from beamline BM16 at the European Synchrotron Radiation Facility (ESRF, Grenoble, France) that were used for structure analysis of BipD from <em>Burkholderia pseudomallei</em>. Details of the data collection are given in the included scanned notes. </p>
Loess and anthropogenic activities enable moderate-sized earthquakes to induce anomalous hazards
<p>Raw data of "Loess and anthropogenic activities enable moderate-sized earthquakes to induce anomalous hazards"</p>
Dataset for: Anomalous Subkelvin Thermal Frequency Shifts of Ultranarrow Linewidth Solid State Emitters
<p>Dataset supporting figures for the peer-reviewed article, "Anomalous Subkelvin Thermal Frequency Shifts of Ultranarrow Linewidth Solid State Emitters".</p>
Superior robustness of anomalous nonreciprocal topological edge states
<p>Figure data, raw measured data, and codes for the paper "Superior robustness of anomalous nonreciprocal topological edge states", Nature 2021, by Z. Zhang et al.</p>
Anomalous datasets for 6UG0 and 6VXT
<p><strong>1. Anomalous map coefficient files (*.pha) in a format understood by the program Coot (<em>Emsley P, 2010</em>) for display of the density.</strong></p> <p><strong>2. CCP4 map files (*.map) in order to display in PyMol (The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.).</strong></p> <p><strong>3. A list of attachments</strong></p> <p> a. File name: 6UG0_Fig_1c_1f_Data_collected_at_7140eV</p> <p> - The electron density in fig 1 (C) and (F)</p> <p> b. File name: 6UG0_Fig_2a_2d_Data_collected_at_7100eV</p> <p> - The electron density in fig 2 (A) and (D)</p> <p> c. File name: 6UG0_Fig_S7b_S7d_Data_collected_at_7100eV</p> <p> - The electron density in fig S7 (B) and (D)</p> <p> d. File name: 6VXT_Fig_4b_4d_Data_collected_at_7100eV</p> <p> - The electron density in fig 4 (B) and (D)</p>
Vanadium nitrogenase - anomalous maps for resting and turnover states
<p>The data set contains anomalous difference electron density maps (in CCP4 format) for two published crystal structures of <em>Azotobacter vinelandii</em> vanadium-dependent nitrogenase. These are:</p> <p>1. VFe protein - resting state. <strong>PDB-ID 5N6Y</strong>. Sippel <em>et al. </em>(2017) <em><strong>Nature Chem. Biol.</strong></em> 13, 956-960. DOI: 10.1038/Nchembio.2428</p> <p>2. VFe protein - turnover state. <strong>PDB-ID 6FEA</strong>. Sippel et al. (2018) <em><strong>Science</strong></em> 359, 1484-1489. DOI: 10.1126/science.aar2765</p> <p>For each deposition, the maps provided are calculated with anomalous differences as Fourier coefficients, to 1.9 Å or 2.0 Å resoution, respectively. Depending on energies of data collection, these maps were used to visualize:</p> <p>12398 eV - anomalous contirbution of iron ions</p> <p>7000 eV - anomalous contribution of sulfur atoms (with strong contribution of V)</p> <p>6200 eV - improved signal for S/Fe and S/V, respectively (not shown in publications).</p>
On the anomalous shapes of native copper crystals from the Michigan Copper Country
<p>Raw NanoSIMS image data files and metadata files with analytical conditions for each analysis. Data associated with manuscript "On the anomalous shapes of native copper crystals from the Michigan Copper Country" by Boulliard, Aléon and Gaillou, accepted in European Journal of Mineralogy, 2021.</p>
Data from: Clots reveal anomalous elastic behavior of fiber networks
<p>The adaptive mechanical properties of soft and fibrous biological materials are relevant to their functionality. The emergence of the macroscopic response of these materials to external stress and intrinsic cell traction from local deformations of their structural components is not well understood. Here, we investigate the nonlinear elastic behavior of blood clots by combining microscopy, rheology, and an elastic network model that incorporates the stretching, bending, and buckling of constituent fibrin fibers. By inhibiting fibrin crosslinking in blood clots, we observe an anomalous softening regime in the macroscopic shear response as well as a reduction in platelet-induced clot contractility. Our model explains these observations from two independent macroscopic measurements in a unified manner, through a single mechanical parameter, the bending stiffness of individual fibers. Supported by experimental evidence, our mechanics-based model provides a framework for predicting and comprehending the nonlinear elastic behavior of blood clots and other active biopolymer networks in general.</p>
Anomalous and Chern topological waves in hyperbolic networks
<p>Data and codes for the paper "Anomalous and Chern topological waves in hyperbolic networks", by Q. Chen et al.</p>
Mechanisms for Layered Anisotropy and Anomalous Magmatism of Alaska Subduction System Revealed by Ambient Noise Tomography and the Wave Gradiometry Method
<p>Depth Coverage: 2.0 - 230.0 km<br>Areal Coverage: Latitude: 54.8 to 72.0 Longitude: -168.0 to -129.0<br>Model Description: A high-resolution 3D azimuthal anisotropic shear wave velocity model beneath Alaska and its surroundings on a ~50‐km grid extracted by ambient noise tomography and wave gradiometry method .</p> <p>Columns in the subfile, from left to right, are latitude, longitude, shear wave velocity, fast direction, and the magnitude of anisotropy, respectively. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.